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IBMR Assignment 1

This assignment focuses on constructing photo mosaics by stitching multiple images together. Key steps include corner detection, perspective mapping, and image blending. Students are required to shoot their own photos with significant overlap (40% to 70%) and utilize projective warping techniques to align and merge images into a seamless mosaic. Optional advanced techniques such as SIFT, multi-band blending, and various mapping methods can earn bonus credit. The project emphasizes hands-on application, mathematical modeling, and image processing skills.

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IBMR Assignment 1

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  1. IBMR Assignment 1 Stitching Photo Mosaics

  2. What is Photo Mosaic? • Stitching photos to construct a wild-view scene. • Part1: CORNER DETECTION • Part2: PERSPECTIVE MAPPING and MOSAICING • Handout after Part2 Finished

  3. PERSPECTIVE MAPPING and MOSAICING

  4. Requirement • Read n>2 images, and create an image mosaic by registering, projective warping, resampling, and compositingthem. • (bonus) multiband blending, SIFT ,panorama or other methods mentioned in class.

  5. Just overlapping

  6. Steps • Shoot the Pictures • Recover Homographies • Warp the Images/ Image Rectification • Gain Compensation • Blend the images into a mosaic

  7. Shoot the Pictures • You may use the photos on the webpage, but shoot your own photos and mosaic them will get bonus credit. • Shoot photos as: • Overlap the fields of view significantly. 40% to 70% overlap is recommended.

  8. Recover Homographies • Construct a linear system as: p’=Hp, where p’ and p are correspondence points. • Follow the Lecture 8 page 6~9. You may try Affine mappings(DOF=6) or Projective mappings(DOF=8). • Solve Ax=0

  9. Warp the Images/Image Rectification • Source scanning(forward mapping) or destination scanning(inverse mapping). • You will need to avoid aliasing when resampling the image. • Be careful of the size of the resulting image.

  10. Gain Compensation • Find the optimize gains of giaccording to means of overlapping regions between image pair i and j.

  11. Blend the images into a mosaic • Linear blending by the weights: where w(x) varies linearly from 1 at the centreof the image to 0 at the edge. • Multi-band blending (bonus): A B

  12. Multi-band blending • Band 1 scale 0 to σ • Band 2 scale σ to 2σ • Band 3 lower than 2σ

  13. Support • Your own project1a code. • A C called matlab library. • to calculate inverse matrix , SVD or etc.

  14. Result

  15. Grading • Basic: 75% • Harris Corner Detection + KNN (Hw1a) • RANSAC • Projection Mapping / Affine Mapping • Image Warping • Bonus: • Non-Maximum Suppression 5% • KD Tree 5% • SIFT 15% • Gain Compensation 10% • Linear Blending 5% • Multi Blending 10% • Stitching your own photos 5% • Others

  16. Deadline • 11/22 11:59:59pm • Upload your program & report to: • host : caig.cs.nctu.edu.tw • port : 30021 • username : IBMR10 • password : IBMR10 • and create your own folder with your ID.

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